1. Instituto Universitario Politécnico
“Santiago Mariño”
Extensión Porlamar
Fundamentals and literature review of wavelet
transform in power quality issues.
(Macroestructura)
Realizador por:
Carlianis Rojas R.
C.I.:24.105.438
SAIA 3A

2. MICRO-POSICION
Top Ideas
Fundamentals and literature review of wavelet transform in power
quality issues.
The term wavelet means a small wave. In addition, a wave provided that this
function is an oscillatory concerns. The other end of the Wavelet Transform
(WT) is the mother wavelet. This implies that the functions used in the
processes of decomposition and reconstruction are derived from a main
function or wavelet. In other words, the mother wavelet is a prototype for
generating the other wavelet functions. The translation time (τ) is used in the
same sense as it is used in a short time Fourier transform (STFT).
Continuous wavelet transform .

3. Unlike FT, CWT has the ability to construct a time-frequency of a signal that
offers very good time and frequency localization representation. In other
words, CWT is used to divide a function of continuous time intervals of equal
time and equal frequency intervals. In CWT, the signal is multiplied with
wavelets, same as STFT and the transform is calculated separately for
different parts of the time domain signal.
Haar Wavelet
There are two features that play a major role in the wavelet analysis. These
are the function (φ) of scale and wavelet (ψ). These functions generate a
family of functions that can be used to break or reconstruct the signal. To
emphasize marriage involved in the construction of this family, φ is
sometimes called as wavelet as father and mother wavelet ψ (Boggess,
2001).

4. Daubechies Wavelet
Ingrid Daubechies in 1987 sought a wavelet family that had compact support
and a kind of softness. The scaling function of its discovery was quite a feat,
and was enthusiastically received. There are three requirements for the
following example of Daubechies wavelets. Have the Daubechies wavelets
compact support, orthogonality and regularity conditions. These properties
are to be required in constructing the scaling function, if the accuracy is
Desired. HENCE, the mother wavelet and all children can be approximated as
well. This Indicates That It is not Necessary to know to específicamente
formulated for the scaling function in order to not work with wavelets. Other
wavelet families are developed in This Way (Aboufadel et al, 1999. Addison,
2002).

5. Discrete wavelet transform
DWT is the form of CWT that uses the discrete values of the signal in the
time-domain. The control parameters m and n contain the set of positive and
negative integers. WT of a continuous signal x(t) gives the decomposition
wavelets by using the discrete form of (1). In other words, the scale-location
of the signal in the time-frequency-domain is determined by using (8).
Advantages and disadvantages of wt with fft

6. Many transforms have been devised by engineers and mathematicians such
as the Laplace transform, the Hilbert transform, Z-transform and FT.
However, FT is the most popular among these transforms. It presents lots of
limitations in many areas of study. Only one of them is present at a time. The
frequency-domain gives no information about the time of signal generation or
the time at which the frequencies are occurred (Bousaleh et al., 2009). The
points of similarity and contrast between WT and FT are given in the following
(Sarıbulut, 2012).
MACRO-POSICION
Identifications and clarifications, exemplifications or additions:
(Review of the literature on wavelet transform power quality issues)
Recent studies related to the WT are summarized survey of the literature on:
DQ algorithm key conventional synchronous-frame is changed in filtering
process (Firouzjah et al., 2009). As an improvement, the low pass filter to
Fourier basis is replaced by a wavelet method windows. The appearance of
windows is adopted to reduce the Hamming window effect conventional
rectangular window in the frequency domain. The classification scheme of the
perturbation is performed with a wavelet neural network (Uyar et al., 2008).
Feature extraction and classification algorithm comprises a feature extractor
based on wavelet entropy based classifier rule and multilayer perceptron is
performed. A model of fault location of transmission line based on an Elman
recurrent network was presented for short circuit faults balanced and
unbalanced (Ekici et al., Saribulut 5-2009). WT is used for the selection of the
hallmarks of faulty signals. The fault signals are analyzed using DWT divides
the signal into detail and approximation coefficients. The energies of detail
coefficients fed neural networks to estimate the fault location. The recognition
of disturbances is discussed PQ (Kaewars et al., 2008). The neural classifier

7. based on multi-wave-is used to automatically detect, locate and classify the
type of transients high precision and low use time. The PQDs WT and
classification system based on learning network of self-organization (He et al.,
2006) is proposed. WT is used to extract feature vectors for different
disturbances PQ based multiresolution analysis. Then, these feature vectors
are applied to a solar system for training and testing.
PRESENTACIÓN EN POWER POINT
http://www.slideshare.net/manurv4/nuevo-presentacin-de-microsoft-
power-point-35709601